Actigraphy offers one of the best-known alternatives to polysomnography for sleep–wake identification. The advantages of actigraphy include highaccuracy, simplicity of use and low intrusiveness. These features allow the use of actigraphy for determining sleep–wake states in such highly sensitivegroups as infants. This study utilizes a motion sensor (accelerometer) for a dual purpose: to determine an infant’s position in the crib and to identifysleep–wake states. The accelerometer was positioned over the sacral region on the infant’s diaper, unlike commonly used attachment to an ankle. Opposedto broadly used discriminant analysis, this study utilized logistic regression and neural networks as predictors. The accuracy of predicted sleep–wakestates was established in comparison to the sleep–wake states recorded by technicians in a polysomnograph study. Both statistical and neural predictors ofthis study provide an accuracy of approximately 77–92% which is comparable to similar studies achieving prediction rates of 85–95%, thus validating thesuggested methodology. The results support the use of body motion as a simple and reliable method for determining sleep–wake states in infants. Nonlinear mapping capabilities of the neural network benefit the accuracy of sleep–wake state identification. Utilization of the accelerometer for the dual purpose allows us to minimize intrusiveness of home infant monitors.